Automation in the workplace is an expensive endeavor

When it comes to the threat of automation, I agree with Ryan Khurana: “From self-driving car crashes to failed workplace algorithms, many AI tools fail to perform simple tasks humans excel at, let alone far surpass us in every way.” Like myself, he is skeptical that automation will unravel the labor market, pointing out that “[The] conflation of what AI ‘may one day do’ with the much more mundane ‘what software can do today’ creates a powerful narrative around automation that accepts no refutation.”

Khurana marshals a number of examples to make this point:

Google needs to use human callers to impersonate its Duplex system on up to a quarter of calls, and Uber needs crowd-sourced labor to ensure its automated identification system remains fast, but admitting this makes them look less automated…

London-based investment firm MMC Ventures found that out of the 2,830 startups they identified as being “AI-focused” in Europe, 40 percent used no machine learning tools, whatsoever.

I’ve been collecting examples of the AI hype machine as well. Here are some of my favorites.

From Rodney Brooks comes this corrective: “Chris Urmson, the former leader of Google’s self-driving car project, once hoped that his son wouldn’t need a driver’s license because driverless cars would be so plentiful by 2020. Now the CEO of the self-driving startup Aurora, Urmson says that driverless cars will be slowly integrated onto our roads “over the next 30 to 50 years.”

Judea Pearl, a pioneer in statistics, said last year that “All the impressive achievements of deep learning amount to just curve fitting,” a technique that was developed decades ago.

Earlier this year, IBM shut down its Watson AI tool for drug discovery.

Mike Mallazzo said it this way: “The investors know it’s bullshit. When venture capitalists say they are looking to add ‘A.I. companies’ to their portfolio, what they really want is a technological moat built around access to uniquely valuable data. If it’s beneficial for companies to sprinkle in a little sex appeal and brand this as ‘A.I.,’ there’s no incentive to stop them from doing so.”

And there is the problem of cost:

As I explained before, the large pecuniary costs in big data technologies don’t speak to the equally expensive task of overhauling management techniques to make the new systems work. New technologies can’t be seamlessly adopted within firms, they need management and process innovations to make the new data-driven methods profitable. And to be honest, we just aren’t there yet.